A solar radio dynamic spectrograph with flexible temporal-spectral resolution

Observation and research on solar radio emission have unique scientific values in solar and space physics and related space weather forecasting applications, since the observed spectral structures may carry important information about energetic electrons and underlying physical mechanisms. In this study, we present the design of a novel dynamic spectrograph that has been installed at the Chashan Solar Radio Observatory operated by the Laboratory for Radio Technologies, Institute of Space Sciences at Shandong University. The spectrograph is characterized by real-time storage of digitized radio intensity data in the time domain and its capability to perform off-line spectral analysis of the radio spectra. The analog signals received via antennas and amplified with a low-noise amplifier are converted into digital data at a speed reaching up to 32 k data points per millisecond. The digital data are then saved into a high- speed electronic disk for further off-line spectral analysis. Using different word lengths （1-32k） and time cadences （5 ms-10 s） for off-line fast Fourier transform analysis, we can obtain the dynamic spectrum of a radio burst with different （user-defined） temporal （5 ms-10 s） and spectral （3 kHz-320kHz） resolutions. This enables great flexibility and convenience in data analysis of solar radio bursts, especially when some specific fine spectral structures are under study.

Solar radio filtering algorithm based on improved long short-term memory

The effective observation of burst events in solar radio research has been impeded by various interference signals,especially interference signals with a wide frequency range and high intensity,as they can partially or completely obscure the observation of burst events.Image processing methods that directly remove the interference signal channels and subtract the average of the interference signal channel are not suitable for processing all types of interference signals.This paper proposes the use of a specific kind of recurrent neural networks,called long short-term memory networks,to predict the value of the radio frequency interference signals with high intensity of the burst event in the solar radio spectrum.The predicted interference can then be removed in accordance with the principle that signals can be linearly added.Therefore,predicted value is subtracted from the data containing the burst event signals and the RFI signals(The radio frequency interference signals to be processed in this article refer to the signal of the broadcast signal that can be received in the frequency range,the signal transmitted by the mobile phone,and the signal transmitted by the sea vessel,and the like)to remove the interference.Then,in order to reduce the error caused by the stepwise prediction in the network and further improve the prediction accuracy,this paper analyzes the characteristics of the value of the radio interference and applies the digital mapping method to convert the prediction problem into the classification problem in the time series.The experimental results show that the proposed method can effectively remove the radio interference in the solar spectrum and clearly show the burst events.

射电天文站电磁环境测量方法及分析

射电天文业务是接收来自遥远宇宙天体的射电信号进行研究的科学。由于来自宇宙天体的射电天文信号非常微弱,比通信系统的信号暗弱百万倍,使射电天文望远镜对周围电磁环境的要求极其严格。射电天文站址要选在电磁环境良好的地区,并在以后的运行中得到电磁环境的保护。建立一套测试射电天文站址电磁环境的设备和方法,对射电天文站址的电磁环境进行实际测试,并对实测数据进行科学合理地分析、评价,作为以后射电天文观测和保护电磁环境研究的依据都是非常重要的。

用小波方法处理射电天文数据

本文介绍了小波分析的基本原理及其在射电天文数据处理中的初步应用。

高速同步数据采集系统性能测试方法研究

在射电天文观测中,天文信号十分微弱,在大部分情况下可以看成是噪声功率的微小增加,需要长的积分时间才能从噪声中显现,因此在进行射电天文数据采集时对采集系统的性能具有较高的要求。针对射电天文数据的特点,从噪声特性、频率特性和同步特性3方面给出了系统性能的衡量指标,围绕这些指标,分别研究了其测试方法,最后以某个实际射电天文数据采集系统为测试平台,进行了指标的测试。测试结果验证了所提指标的有效性和指标测试方法的合理性。